Measuring the cognitive effort associated with task switching in routine EHR-based tasks.

OBJECTIVE: Clinical work involves performing overlapping, time-sensitive tasks that frequently require clinicians to switch their attention between multiple tasks. We developed a methodological approach using EHR-based audit logs to determine switch costs-the cognitive burden associated with task switching-and assessed its magnitude during routine EHR-based clinical tasks. METHOD: Physician trainees (N = 75) participated in a longitudinal study where they provided access to their EHR-based audit logs. Physicians' audit log actions were used to create a taxonomy of EHR tasks. These tasks were transformed into task sequences and the time spent on each task in a sequence was computed. Within these task sequences, instances of task switching (i.e., switching from one task to the next) and non-switching were identified. The primary outcome of interest was the time spent on a post-switch task. Using a mixed-effects regression model, we compared the durations of post-switch and non-switch tasks. RESULTS: 2,781,679 audit log events over 117,822 sessions from 75 physicians were analyzed. Physicians spent most time on chart review (Median (IQR) = 5,439 (2,492-8,336) seconds), note review (1,936 (827-3,321) seconds), and navigating the EHR interface (1,048 (365.5-2,006) seconds) daily. Post task switch activity times were greater for documentation (Median increase = 5 s), order entry (Median increase = 3 s) and results review (Median increase = 3 s). Mixed-effects regression showed that time spent on tasks were longer following a task switch (ß = 0.03; 95% CIlower = 0.027, CIupper = 0.034), with greater post-swtich task times for imaging, order entry, note review, handoff, note entry, chart review and best practice advisory tasks. DISCUSSION: Increased task switching time-an indicator of the cognitive burden associated with switching between tasks-is prevalent in routine EHR-based tasks. We discuss the cumulative impact of incremental switch costs have on overall EHR workload, wellness, and error rates. Relying on theoretical cognitive foundations, we suggest pragmatic design considerations for mitigating the effects of cognitive burden associated with task switching.

Integrating machine learning predictions for perioperative risk management: Towards an empirical design of a flexible-standardized risk assessment tool.

BACKGROUND: Surgical patients are complex, vulnerable, and prone to postoperative complications that can potentially be mitigated with quality perioperative risk assessment and management. Several institutions have incorporated machine learning (ML) into their patient care to improve awareness and support clinician decision-making along the perioperative spectrum. Recent research suggests that ML risk prediction can support perioperative patient risk monitoring and management across several situations, including the operating room (OR) to intensive care unit (ICU) handoffs. OBJECTIVES: Our study objectives were threefold: (1) evaluate whether ML-generated postoperative predictions are concordant with clinician-generated risk rankings for acute kidney injury, delirium, pneumonia, deep vein thrombosis, and pulmonary embolism, and establish their associated risk factors; (2) ascertain clinician end-user suggestions to improve adoption of ML-generated risks and their integration into the perioperative workflow; and (3) develop a user-friendly visualization format for a tool to display ML-generated risks and risk factors to support postoperative care planning, for example, within the context of OR-ICU handoffs. METHODS: Graphical user interfaces for postoperative risk prediction models were assessed for end-user usability through cognitive walkthroughs and interviews with anesthesiologists, surgeons, certified registered nurse anesthetists, registered nurses, and critical care physicians. Thematic analysis relying on an explanation design framework was used to identify feedback and suggestions for improvement. RESULTS: 17 clinicians participated in the evaluation. ML estimates of complication risks aligned with clinicians' independent rankings, and related displays were perceived as valuable for decision-making and care planning for postoperative care. During OR-ICU handoffs, the tool could speed up report preparation and remind clinicians to address patient-specific complications, thus providing more tailored care information. Suggestions for improvement centered on electronic tool delivery; methods to build trust in ML models; modifiable risks and risk mitigation strategies; and additional patient information based on individual preferences (e.g., surgical procedure). CONCLUSIONS: ML estimates of postoperative complication risks can provide anticipatory guidance, potentially increasing the efficiency of care planning. We have offered an ML visualization framework for designing future ML-augmented tools and anticipate the development of tools that recommend specific actions to the user based on ML model output.

In search of on-line locality effects in sentence comprehension.

Many comprehension theories assert that increasing the distance between elements participating in a linguistic relation (e.g., a verb and a noun phrase argument) increases the difficulty of establishing that relation during on-line comprehension. Such locality effects are expected to increase reading times and are thought to reveal properties and limitations of the short-term memory system that supports comprehension. Despite their theoretical importance and putative ubiquity, however, evidence for on-line locality effects is quite narrow linguistically and methodologically: It is restricted almost exclusively to self-paced reading of complex structures involving a particular class of syntactic relation. We present 4 experiments (2 self-paced reading and 2 eyetracking experiments) that demonstrate locality effects in the course of establishing subject-verb dependencies; locality effects are seen even in materials that can be read quickly and easily. These locality effects are observable in the earliest possible eye-movement measures and are of much shorter duration than previously reported effects. To account for the observed empirical patterns, we outline a processing model of the adaptive control of button pressing and eye movements. This model makes progress toward the goal of eliminating linking assumptions between memory constructs and empirical measures in favor of explicit theories of the coordinated control of motor responses and parsing.